Automated detection of sleep and waking states

ABSTRACT

Determining low power frequency range information from spectral data. Raw signal data can be adjusted to increase dynamic range for power within low power frequency ranges as compared to higher-power frequency ranges to determine adjusted source data valuable for acquiring low power frequency range information. Low power frequency range information can be used in the analysis of a variety of raw signal data. For example, low power frequency range information within electroencephalography data for a subject from a period of sleep can be used to determine sleep states. Similarly, automated full-frequency spectral electroencephalography signal analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, and determining the effect of medication on sleep states.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 60/679,951, filed on May 10, 2005. The disclosure of the priorapplication is considered part of (and is incorporated by reference in)the disclosure of this application.

BACKGROUND

Sleep states and other brain activity have been commonly analyzed viaelectroencephalography or EEG signals. As a person falls asleep, thebrain activity is modulated, representing different depths and phases ofsleep. In a typical person, the sleep states transition over time,starting at a first sleep state known as slow wave sleep or SWS. SWS haslow frequency high power EEG activity. The sleep may lighten intoso-called intermediate sleep states. Other sleep states known as rapideye movement sleep is characterized by a lower power EEG activity.

EEG signals follow a distribution where higher frequency signals havelower amplitudes and therefore lower power. This so-called 1/fdistribution means that the highest amplitudes are present at the lowestfrequencies.

EEG signals for sleep stage determination are conventionally analyzedusing the Rechtschaffen-Kales method. This method can rely on manuallyscoring sleep EEG signals due to the low power frequency limitations ofautomated signal analysis techniques. The Rechtschaffen-Kales method canbe both highly unreliable and time consuming because statisticallysignificant shifts at high frequencies are usually not detectable by ahuman scorer due to the very low amplitudes. Further, theRechtschaffen-Kales method tends to have poor temporal and spatialresolution, does not make all of its variables known, and commonly leadsto low inter-user agreement rates across both manual as well asautomated scorers. Unfortunately, alternative sleep state determinationmethods, including artificial neural network classifiers, usually relyon multiple channels and tend to emulate human performance, therebyimproving the time of determination without drastically improvingquality.

SUMMARY

The present application describes normalizing data indicative ofbrainwave activity to increase the dynamic range of information withinthe data.

The embodiments explain using this information to determine sleep statesautomatically. Other applications are described which automaticallyassess sleep quality, pathological conditions, and medication effects.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary system for determining lowpower frequency information from source data with at least one low powerfrequency range;

FIG. 2 is a flowchart showing an exemplary method for adjusting sourcedata;

FIG. 3 is a flowchart showing an exemplary method for adjusting sourcedata to account for differences in power over a spectrum of frequenciesover time;

FIG. 4 is a block diagram of an exemplary system for determining sleepstate information for a subject;

FIG. 5 is a block diagram of another exemplary system for determiningsleep state information for a subject;

FIG. 6 is a flowchart showing an exemplary method for determining sleepstates in a subject;

FIG. 7 is a flowchart showing an exemplary method for classifying sleepstates in a subject;

FIG. 8 is a block diagram of an exemplary system for determining apathological condition of a subject from sleep states;

FIG. 9 is a flowchart showing an exemplary computer-implemented methodfor determining a pathological condition for a subject based on sleepstates;

FIG. 10 is a block diagram of an exemplary system for dynamicallydetermining customized sleep scores for a subject;

FIG. 11 is a screen shot of an exemplary whole night EEG source datafrequency power spectrogram;

FIG. 12 is a screen shot of the exemplary whole night EEG source datashown in FIG. 11 after an exemplary adjustment technique has beenapplied;

FIG. 13 is a screen shot of a two hour time frame of the exemplaryadjusted whole night EEG source data shown in FIG. 12;

FIG. 14 is a screen shot of an exemplary visualization of high and lowpower frequency bands within the whole night EEG spectrogram shown inFIG. 12;

FIG. 15 is a screen shot of a two hour and forty minutes time frame ofthe exemplary visualization of high and low power frequency bands withinthe whole night spectrogram shown in FIG. 14.

FIG. 16 is a screen shot of an exemplary five-dimensional parameterspace visualization of the whole night EEG spectrogram of FIG. 12;

FIG. 17 is a screen shot of a two hour time frame of the exemplaryfive-dimensional parameter space visualization of the whole night EEGvisualization shown in FIG. 16;

FIG. 18 is a screen shot of an exemplary visualization of classifiedsleep states based on EEG spectrogram data;

FIG. 19 is a screen shot of another exemplary visualization ofclassified sleep states based on EEG spectrogram data;

FIG. 20 is a screen shot of yet another exemplary visualization ofclassified sleep states based on EEG spectrogram data;

FIG. 21 is a screen shot from another vantage point of the exemplaryvisualization of classified sleep states based on EEG spectrogram dataof FIG. 20;

FIGS. 22, 23, 24 and 25 are screen shots of canonical spectrarepresentative of frequency weighted epochs designated as distinct sleepstates in a subject for a period of time;

FIG. 26 is a screen shot of a canonical spectra representative of afrequency weighted epoch that displays a transient sleep state havingcharacteristics of more than one sleep state;

FIG. 27 is a screen shot of an exemplary visualization of the degree ofsleep stager separation that distinguishes representative canonicalspectra of distinct sleep state;

FIGS. 28, 29, 30, 31 and 32 are screen shots of exemplary visualizationof sleep state statistics for a subject according to sleep statedesignations of one or more epochs;

FIG. 33 is a screen shot of an exemplary visualization of classifiedanesthesia states of an anesthetized cat based on EEG spectrogram data;

FIG. 34 is a screen shot of an exemplary visualization of classifiedsleep states of a human subject based on EEG spectrogram data;

FIG. 35 is a flowchart showing yet another exemplary method forclassifying sleep states in a subject that can be implemented with thedescribed technologies;

FIG. 36 is an exemplary computer system that can be implemented with thedescribed technologies;

FIG. 37 is a screen shot of an exemplary visualization of independentcomponent analysis applied on a normalized spectrogram to furtherdetermine appropriate frequency windows for extracting information;

FIG. 38 is a screen shot of an exemplary visualization of independentcomponents of FIG. 37 throughout time;

FIG. 39 is a screen shot of a six and a half hour time frame of anexemplary five-dimensional parameter space visualization of frequencybands of the whole night EEG visualization from a human subject withAlzheimer's;

FIG. 40 is a screen shot of an exemplary visualization of classifiedunihemispheric sleep from a bird;

FIG. 41 illustrates a flowchart of operation of another embodiment whichuses a double normalization;

FIGS. 42 a-42 c show the raw spectrogram, single normalized spectrogram,and double normalized spectrogram respectively;

FIG. 43 shows the preferred frequency over time;

FIG. 44 shows a diagram of these frequencies;

FIG. 45 shows a three-dimensional view of the data; and

FIG. 46 shows a graph of spectral fragmentation for the frequencies.

DETAILED DESCRIPTION

One important recognition of the present system is that the lowfrequency ranges in EEG signals often have the most energy, and hencehave mistakenly led many researchers to overanalyze that low frequencyrange. However, one reason found for the increased power in those lowerfrequencies, was found by the inventors to be the low-passcharacteristic of the skull. Other reasons may also contribute to theincreased power in lower frequencies.

Obtained EEG signals are low-power frequency signals and follow a 1/fdistribution, whereby the power in the signal is inversely related,e.g., inversely proportional, to the frequency.

EEG signals have typically been examined in time in series incrementscalled epochs. For example, when the EEG signal is used for analyzingsleep, sleep may be segmented into one or more epochs to use foranalysis. The epochs can be segmented into different sections using ascanning window, where the scanning window defines different sections ofthe time series increment. The scanning window can move via a slidingwindow, where sections of the sliding window have overlapping timeseries sequences. An epoch can alternatively span an entire time series,for example.

According to the present application, different forms of sleep state maybe monitored. A sleep state is described as any distinguishable sleep orwakefulness that is representative of behavioral, physical or signalcharacteristics. Sleep states which are referred to in this applicationinclude slow wave sleep or SWS, rapid eye movement sleep or REM,intermediate sleep states also called inter or IS states, and awakestates. Awake states may actually be part of the sleep state, and theawake states can be characterized by vigilance into attentiveness orlevels of alertness. The intermediate sleep can also be characterized asintermediate-1 sleep and intermediate-2 sleep.

An artifact may also be obtained during acquisition of an EEG. Anartifact is data that misrepresents the EEG. For example, movementwithin a user that registers on the EEG may be an artifact. Exampleartifacts include muscle twitches and the like.

EXAMPLE 1 Exemplary Source Data

In any of the embodiments described herein, a variety of source data canbe analyzed including electroencephalography (EEG) data,electrocardiography data (EKG), electrooculography data (EOG),electromyography data (EMG), local field potential (LFP) data, spiketrain data, wave data including sound and pressure waves, and any dataexhibiting where there are differences in dynamic range of power forvarious frequencies across a frequency spectrum of the data e.g., a 1/fdistribution. Source data can include encoded data stored at low powerfrequency within source data.

EXAMPLE 2 Exemplary System for Determining Low Power FrequencyInformation from Source Data with at Least One Low Power Frequency Range

FIG. 1 shows an exemplary system 100 for determining low power frequencyinformation from source data with at least one low power frequencyrange.

Source data with at least one low power frequency range 102 is obtainedand input into software 104 to determine low power frequency information106.

The software 104 can employ any combination of technologies, such asthose described herein, to determine low power frequency information 106for the source data.

Methods for determining low power frequency information from source datawith at least one low power frequency range are described in detailbelow.

EXAMPLE 3 Exemplary Method for Adjusting Source Data

FIG. 2 shows an exemplary method 200 for adjusting source data. Forexample, the method 200 can be implemented within system 100 of FIG. 1.

At 202, source data with at least one low power frequency range isreceived. For example, electroencephalography source data for a subjectcan be received. Source data can be received via a single channel ormultiple channels.

At 204, source data is adjusted to increase the dynamic range for powerwithin at least one low power frequency range of the frequency spectrumof the source data as compared to a second higher power frequency range.A number of adjustment techniques described herein, includingnormalization and frequency weighting can be used. In an embodiment,electroencephalography source data is normalized to increase the lowpower, higher frequency range data relative to the higher power, lowerfrequency range data or, more generally, to normalize the powers of thedifferent signal parts.

After the source data is adjusted, various other processing can be done.For example, a visualization of the adjusted source data can bepresented. Further, low power frequency information can be extractedfrom the adjusted source data. For example, low power frequencyinformation can be extracted from adjusted electroencephalography sourcedata. Higher power frequency information can also be extracted from theadjusted source data.

The method described in this or any of the other examples can be acomputer-implemented method performed via computer-executableinstructions in one or more computer-readable media. Any of the actionsshown can be performed by software incorporated within a signalprocessing system or any other signal data analyzer system.

EXAMPLE 4 Exemplary Method for Adjusting Source Data to Account forDifferences in Power over a Spectrum of Frequencies Over Time

FIG. 3 shows an exemplary method 300 for adjusting source data toaccount for differences in power over a spectrum of frequencies overtime. For example, the method 300 can be implemented within system 100of FIG. 1.

At 302, source data with at least on low power frequency range isreceived. For example, electroencephalography data with at least one lowpower frequency range can be received. Artifacts in the data can beremoved from the source data. For example, artifact data can be manuallyremoved from the source data or automatically filtered out of sourcedata via a filtering (e.g., DC filtering) or data smoothing technique.The source data can also be pretreated with component analysis.

At 304, the source data is segmented into one or more epochs; where eachepoch is a portion of data from the series. For example, the source datacan be segmented into a plurality of time segments via a variety ofseparating techniques. Scanning windows and sliding windows can be usedto separate the source data into time series increments.

At 306, the one or more epochs are normalized for differences in powerof the one or more epochs across time. For example, the power of eachepoch at one or more frequencies can be normalized across time todetermine appropriate frequency windows for extracting information. Suchnormalization can reveal low power, statistically significant shifts inpower at one or more frequencies (e.g., Delta, Gamma, and the like). Anyfrequency range can be revealed and utilized for analysis. Informationcan be calculated for each of the one or more epochs after appropriatefrequency windows have been established. Such information can includelow frequency power (e.g., Delta power), high frequency power (e.g.,Gamma power), standard deviation, maximum amplitude (e.g., maximum ofthe absolute value of peaks) and the sort. Further calculations can bedone on the information calculated for each of the one or more epochscreating information such as Gamma power/Delta power, time derivative ofDelta, time derivative of Gamma power/Delta power and the like. Timederivatives can be computed over preceding and successive epochs. Aftercalculating the information, that information can then be normalizedacross the one or more epochs. A variety of data normalizationtechniques can be conducted including z-scoring and other similartechniques.

At 308, results of the adjustment of source data to account fordifferences in power over a spectrum of frequencies over time can bepresented as one or more epochs of data. For example, frequency weightedepochs can be presented as adjusted source data.

EXAMPLE 5 Exemplary System for Determining Sleep State

Information for a Subject

FIG. 4 shows an exemplary system 400 for determining sleep stateinformation for a subject. Electroencephalography data for a subject 402is obtained and input into software 404 to determine sleep stateinformation for the subject 406.

The software 404 can employ any combination of technologies, such asthose described herein, to determine sleep state information for thesubject 406.

Methods for determining sleep state information for a subject aredescribed in detail below.

EXAMPLE 6 Another Exemplary System for Determining Sleep StateInformation for a Subject

FIG. 5 shows an exemplary system 500 for determining sleep stateinformation for a subject.

Electroencephalography data for a subject 502 is obtained and input intosegmenter 504 to segment the data into one or more epochs. In practice,epochs are of similar (e.g., the same) length. Epoch length can beadjusted via a configurable parameter. The one or more epochs, in turn,are input into normalizer 506 to normalize frequency data in the one ormore epochs across time, thereby frequency weighting the one or moreepochs of electroencephalography data. The one or more frequencyweighted epochs are then input into classifier 508 to classify the datainto sleep states, thereby generating sleep state information for thesubject 510.

Methods for determining sleep state information for a subject aredescribed in detail below.

EXAMPLE 7 Exemplary Method for Determining Sleep States in a Subject

FIG. 6 shows an exemplary method 600 for determining sleep states in asubject. For example, the method 600 can be implemented within system500 of FIG. 5 or system 400 of FIG. 4.

At 602, electroencephalography (EEG) data for a subject is received. Forexample, electroencephalography data, which exhibits lower dynamic rangefor power in at least one low power first frequency range in a frequencyspectrum as compared to a second frequency range in the frequencyspectrum, can be received.

At 604, the electroencephalography data for the subject is segmentedinto one or more epochs. For example, the EEG data can be segmented intoone or more epochs via a variety of separating techniques. Scanningwindows and sliding windows can be used to separate the EEG data intoone or more epochs. The source data can also be filtered via directcurrent filtering during, prior to, or after segmenting. The source datacan also be pretreated with component analysis (e.g., principle orindependent component analysis).

FIG. 11 is a screen shot of an exemplary whole night EEG source datafrequency power spectrogram for a subject that has been segmented overthree second epochs spaced in 1 second increments. Power range isindicated in the shading, where white shaded regions are higher in powerthan dark shaded regions. The higher frequencies (e.g., Gamma) thereforeexhibit lower power than the lower frequencies (e.g., Delta, Theta andthe like) in the whole night EEG data.

At 606, frequency power of the one or more epochs is weighted acrosstime. For example, the power of each epoch at one or more frequenciescan be normalized across time to determine appropriate frequency windowsfor extracting information. Such normalization can reveal low power,statistically significant shifts in power at one or more frequencies(e.g., Delta, Gamma, and the like). Additionally, each epoch can berepresented by the frequency with the highest relative power over timeto determine appropriate frequency windows for extracting information.Alternatively, component analysis (e.g., principle component analysis(PCA) or independent component analysis (ICA)) can be utilized afternormalization to further determine appropriate frequency windows forextracting information. For example, FIGS. 37 and 38 are screen shots ofcomponent analysis utilized after normalization to suggest filters(e.g., screen shot 3700) and express independent components throughouttime (e.g., screen shot 3800). Any frequency range can be revealed andutilized for analysis.

Information can be calculated for each of the one or more epochs afterappropriate frequency windows have been established (e.g., afterweighting frequency). Such information can include low frequency power(e.g., Delta power), high frequency power (e.g., Gamma power), standarddeviation, maximum amplitude (e.g., maximum of the absolute value ofpeaks) and the sort. Further calculations can be done on the informationcalculated for each of the one or more epochs creating information suchas Gamma power/Delta power, time derivative of Delta, time derivative ofGamma power/Delta power and the like. Time derivatives can be computedover preceding and successive epochs. After calculating the information,it can then be normalized across the one or more epochs. A variety ofdata normalization techniques can be conducted including z-scoring andthe like.

FIG. 12 is a screen shot of the exemplary whole night EEG source datashown in FIG. 11 after an exemplary frequency power of the one or moreepochs has been weighted across time. The higher frequency data is nowmore clearly visible. FIG. 13 is a screen shot of a two hour time frameof the exemplary adjusted whole night EEG source data shown in FIG. 12.FIG. 14 is a screen shot of an exemplary visualization of high (e.g.,Gamma) and low (e.g., Delta) power frequency bands within the wholenight EEG spectrogram shown in FIG. 12. FIG. 15 is a screen shot of atwo hour and forty minutes time frame of the exemplary visualization ofhigh and low power frequency bands shown in FIG. 14.

FIG. 16 is a screen shot of an exemplary five-dimensional parameterspace visualization of the whole night EEG spectrogram of FIG. 12. Thefive parameters (e.g., variables) are information calculated for each ofthe one or more epochs after weighting frequency. FIG. 17 is a screenshot of a two hour time frame of the exemplary five-dimensionalparameter space visualization of the whole night EEG visualization shownin FIG. 16.

At 608, sleep states in the subject are classified based on the one ormore frequency weighted epochs. For example, the one or more frequencyweighted epochs can be clustered by any variety of clustering techniquesincluding k-means clustering. The clustering can be done on informationcalculated from the epochs (e.g., Delta power, Gamma power, standarddeviation, maximum amplitude (Gamma/Delta), time derivative of Delta,time derivative of (Gamma/Delta, and the sort). Component analysis(e.g., PCA or ICA) can be used to determine the parameter space (e.g.,types of information used) in the clustering.

Subsequent to clustering, sleep state designations can be assigned tothe epochs. Sleep state designated epochs can then be presented asrepresentations of sleep states in the subject for the period of timerepresented by the epoch. Classification can also incorporate manuallydetermined sleep states (e.g., manually determined “awake” versus“sleeping” sleep states). Additionally, artifact information (e.g.movement data, poor signal data, or the like) can be utilized in theclassification.

EXAMPLE 8 Exemplary Sleep State Classification Techniques

Epochs can be classified according to the sleep states they represent.An epoch can be classified according to normalized variables (e.g.,information calculated for an epoch) based on high frequencyinformation, low frequency information, or both high and low frequencyinformation. For example, REM sleep state epochs can have higherrelative power than SWS at higher frequencies and lower relative powerthan SWS at lower frequencies. Similarly, SWS sleep state epochs canhave lower relative power than REM at higher frequencies and higherrelative power than REM at lower frequencies. Additionally, epochsinitially classified as both NREM and NSWS sleep (e.g., epochs havinglow relative power at both higher and lower frequencies) can beclassified as intermediate sleep and epochs classified as both REM andSWS sleep (e.g., epochs having high relative power at both higher andlower frequencies) can be classified as outliers. Further, epochsinitially classified as both NREM and NSWS sleep can be classified asintermediate stage I sleep and epochs initially classified as both REMand SWS sleep can be classified as intermediate stage II sleep.Additionally, sleep states can be split in the classifying to look forspindles, k-complexes, and other parts. Any group of epochs initiallyclassified as one sleep state can be split into multiple sub-classifiedsleep states according to increasing levels of classification detail.For example, a group of epochs classified as SWS can be reclassified astwo distinct types of

EXAMPLE 9 Exemplary Artifact Classification Techniques

Artifact data (e.g. movement data, poor signal data, and the like) canalso be used in sleep state classification. For example, artifacts canbe used to analyze whether epochs initially assigned a sleep statedesignation should be reassigned a new sleep state designation due toneighboring artifact data. For example, an epoch assigned a sleep statedesignation of REM that has a preceding movement artifact or awake epochcan be reassigned a sleep state designation of awake. Further, forexample, an artifact epoch that has a succeeding SWS epoch can bereassigned a sleep state designation of SWS because there is a highlikelihood that the epoch represents a large SWS sleep epoch rather thana large movement artifact which is more common during wakefulness. Insuch ways, for example, artifact data can be utilized in a datasmoothing technique.

EXAMPLE 10 Exemplary Smoothing Techniques

Any variety of data smoothing techniques can be used during theassigning of sleep states. For example, numbers (e.g., 0 and 1) can beused to represent designated sleep states. Neighboring epochs' sleepstate designation numbers can then be averaged to determine if one ofthe epochs is inaccurately assigned a sleep state designation. Forexample, abrupt jumps from SWS-NSWS-SWS (and REM-NREM-REM) are rare insleep data. Therefore, should a group of epochs be assigned sleep statedesignations representing abrupt jumps in sleep states, smoothingtechniques can be applied to improve the accuracy of the assigning.

For example, in a scenario in which 0 represents SWS, 1 represents NSWSand the following sleep state designations existed for five neighboringepochs, 00100, then an average of the five sleep states would be 0.2. Insuch an instance, the middle epoch initially assigned a sleepdesignation of 1 (SWS) would be reassigned a sleep state designation of0 (NSWS). The same technique could be used for REM versus NREM where asecond set of sleep designations for the same five neighboring epochs isdetermined. For example, 1 can represent REM, 0 can represent NREM, andthe following designations can exit for the five neighboring epochs,00100. Again, the average of the five sleep states would be 0.2. Again,the middle epoch initially assigned a designation of 1 (REM) would bereassigned a sleep state designation of 0 (NREM). Such smoothingtechniques can improve the accuracy of assigning sleep statedesignations.

EXAMPLE 11 Exemplary Method for Classifying Sleep States in a Subject

FIG. 7 shows in a flowchart an exemplary method 700 for classifyingsleep states in a subject. For example, the method 700 can beimplemented within system 500 of FIG. 5, system 400 of FIG. 4 or withinthe classifying 608 of method 600.

At 702, one or more frequency weighted epochs are received. For example,frequency weighted epochs determined from the weighting 606 of method600 can be received.

At 704, the one or more frequency weighted epochs are clustered. Forexample, the one or more frequency weighted epochs can be clustered byany variety of clustering techniques including k-means clustering. Theclustering can be done on information calculated from the epochs (e.g.,Delta power, Gamma power, standard deviation, maximum amplitude(Gamma/Delta), time derivative of Delta, time derivative of Gamma/Delta,and the sort). Exemplary visualizations of clustered sleep states areshown in FIGS. 18 and 19. FIG. 18 shows epochs clustered via Delta,Gamma/Delta, and the time derivative of Delta. In such a manner,REM-like epochs form a visual spear point shape. FIG. 19 shows epochsclustered via Delta, Gamma/Delta, and the time derivative of(Gamma/Delta). In such a manner, SWS-like epochs form a visual spearpoint shape. Additional exemplary visualizations of clustered sleepstates are shown in FIGS. 20 and 21, in which clustering was done usingparameters (e.g., variables) derived via principle component analysis.

At 706, the one or more clustered, frequency weighted epochs areassigned sleep state designations. For example, an epoch withsignificant relative power at low frequency can be assigned a slow wavesleep designation and an epoch with significant relative power at highfrequency can be assigned a rapid eye movement sleep designation. Forexample, REM sleep can have higher Gamma/Delta and a higher absolutevalue of the time derivative of (Gamma/Delta) compared to SWS, whereasSWS can have higher delta and a higher absolute value of the timederivative of delta than REM sleep. Further, for example, standarddeviation can be used in assigning sleep state designations. It ispossible for the same epoch to be assigned both a slow wave sleepdesignation and a rapid eye movement sleep designation. In such cases,the epoch can be reassigned a new sleep state designation of outlier orintermediate stage II sleep. Alternatively, an epoch can be assignedboth a non-slow wave sleep designation and a non-rapid eye movementsleep designation. In such cases, the epoch can be reassigned a newsleep state designation of intermediate sleep or intermediate stage Isleep. For example, when high frequency is expressed by dividing it byDelta and the parameter space Delta, Gamma/Delta,abs(derivative(Delta)), abs(derivative(Gamma/Delta)), and, optionally,standard deviation, then intermediate sleep designation can be theintersection between NREM and NSWS while outlier designation can be theintersection between REM and SWS. Alternatively, for example, if Deltaalone or with standard deviation is used to determine SWS from NSWS andgamma alone or with abs(derivative(Delta)) alone or with standarddeviation is used to determine REM from NREM, then intermediate stage Isleep designation can be the intersection between NREM and NSWS whileintermediate stage II sleep designation can be the intersection betweenREM and SWS. Any variety of data smoothing techniques can be used duringthe assigning of sleep states. Artifact data can also be used during theassigning of sleep states.

At 708, sleep state designations are presented as indicative of sleepstates for the period of time represented by the one or more epochs. Thesleep states can be presented in the form of sleep statistics acrosstime. For example, FIGS. 28, 29, 30, 31, and 32 depict presentations ofsleep statistics for sleep state designated epochs as a function oftime. For example in FIG. 28, a screen shot 2800 depicts sleep statedensity as a percentage for each sleep state type per hour during anight of electroencephalography data for a subject. In FIG. 29, a screenshot 2900 depicts average episode length for each sleep stage acrossevery hour. In FIG. 30, a screen shot 3000 depicts number of episodesfor each sleep stage across every hour. In FIG. 31, a screen shot 3100depicts average time intervals between successive REM sleep stateintervals for each hour. In FIG. 32, a screen shot 3200 depicts stagetransitions across the night.

Additionally, one or more frequency weighted epochs can be presented ascanonical spectra representative of the sleep state in the subject forthe period of time represented by the one or more epochs having similarsleep state designations. For example, an epoch within the middle of agroup of epochs designated as having the same sleep state designationscan be selected and its spectra presented as canonical spectrarepresentative of the sleep state. Alternatively, an epoch having aweighted power closest to the average weighted power of one or moreepochs having similar sleep state designations can be selected and itsspectra presented as canonical spectra representative of the sleepstate. For example, FIGS. 22, 23, 24, 25, and 26 are screen shots ofexemplary visualizations of epochs for various sleep states in a subject(e.g., screen shot 2200 is SWS, screen shot 2300 is REM sleep, screenshot 2400 is Intermediate sleep, screen shot 2500 is awake, and screenshot 2600 is transient) based on EEG spectrogram data analysis.

Additionally, sleep state designations can be presented as a function ofsuccess versus manual scoring and quality measures can be presented(e.g., sleep state designation separation statistics including singlevariable and multivariable one-way ANOVAs, regression coefficientscalculated for each stage for sleep densities, number of episodes,average episode length, cycle time, and the like). An exemplaryvisualization of presenting quality measures is shown in FIG. 27. Ascreen shot 2700 depicts an exemplary visualization of the degree ofsleep stage separation that distinguishes representative canonicalspectra of distinct sleep states. For example, independent componentanalysis (ICA) can be used to establish the quality of sleep stageseparation in the presented sleep states by applying ICA to canonicalspectra or average spectra for each sleep state presented. Any varietyof classifying techniques can be used to determine the quality ofinitially sleep stage classification.

EXAMPLE 12 Exemplary System for Determining a Pathological Condition ofa Subject from Sleep States

FIG. 8 shows an exemplary system 800 for determining a pathologicalcondition of a subject from sleep states.

Electroencephalography data for a subject 802 is obtained and input intosleep state analyzer 804 to determine a pathological condition of thesubject 806.

Methods for determining a pathological condition of a subject from sleepstates exhibited by a subject, as determined from analyzingelectroencephalography data, are described in detail below.

EXAMPLE 13 Exemplary Computer-Implemented Method for Determining aPathological Condition for a Subject from Sleep States

FIG. 9 shows an exemplary computer-implemented method 900 fordetermining a pathological condition for a subject from sleep states.The computer-implemented method 900 can be utilized in system 800 ofFIG. 8.

At 902, electroencephalography data for a subject is received. Forexample, electroencephalography data which exhibits lower dynamic rangefor power in at least one low power first frequency range in a frequencyspectrum as compared to a second frequency range in the frequencyspectrum can be received.

At 904, the electroencephalography data is analyzed with frequencyanalysis. For example, frequency analysis can be the adjusting 204 ofmethod 200.

At 906, sleep states in the subject are assigned based on the frequencyanalysis. For example, method 700 for classifying sleep states of FIG. 7can be used to assign sleep states in the subject.

At 908, a pathological condition can be detected in a subject based onthe sleep states. For example, sleep states can be acquired for anindividual and analyzed to determine whether the sleep states representnormal sleep or abnormal sleep. Abnormal sleep could indicate apathological condition. For example, sleep states can be acquired fromindividuals with pathological conditions and analyzed for commonattributes to generate an exemplary distinctive “pathological condition”sleep state profile and/or sleep state statistics representative ofhaving the pathological condition. Such a profile or statistics can becompared to sleep states determined for a subject in order to detectwhether the subject has the pathological condition or any earlyindicators of the pathological condition. Any variety of pathologicalconditions can be detected and/or analyzed. For example, sleep relatedpathological conditions can include epilepsy, Alzheimer's disease,depression, brain trauma, insomnia, restless leg syndrome, and sleepapnea. For example, polysomnographically, subjects with Alzheimer's canshow decreased rapid eye movement sleep in proportion to the extent oftheir dementia.

EXAMPLE 14 Exemplary System for Dynamically Determining Customized SleepScores for a Subject

FIG. 10 shows an exemplary system for dynamically determining customizedsleep scores for a subject.

A data collector 1002 can obtain electroencephalography data for asubject from a period of sleep.

A data normalizer 1004 can assess the electroencephalography data todetermine low power frequency information.

A data presenter 1006 can present sleep states for the subject based atleast on the low power frequency information.

Methods for dynamically determining customized sleep scores for asubject are described herein, including method 500 of FIG. 5, method 600of FIG. 6, and method 700 of FIG. 7.

EXAMPLE 15 Exemplary Pathological Conditions

In any of the technologies described herein, a variety of pathologicalconditions can be determined from source data obtained for a subject.For example, depression, brain trauma, epilepsy, and Alzheimer's diseasecan be pathological conditions determined from sleep states determinedfrom source data obtained for a subject. For example, FIG. 39 is ascreenshot 3900 of an application of the technologies described hereinto determine sleep states indicative of characterizations of Alzheimer'sdisease from a whole night EEG from a human subject with Alzheimer's.

EXAMPLE 16 Exemplary Medications and Chemicals that can Affect Sleep

In any of the technologies described herein, the effect of medicationsand chemicals on sleep states of a subject can be determined viaanalyzing source data obtained for a subject. For example, sleep statescan be modified by alcohol, nicotine, and cocaine use. Exemplarymedications that affect sleep include steroids, theophylline,decongestants, benzodiazepines, antidepressants, monoamine oxidaseinhibitors (e.g., Pheneizine and Moclobemide), selective serotoninreuptake inhibitors (e.g., Fluoxetine (distributed under the Prozac®name) and Sertralie (distributed under the Zoloft® name), thyroxine,oral contraceptive pills, antihypertensives, antihistamines,neuroleptics, amphetamines, barbiturates, anesthetics, and the like.

EXAMPLE 17 Exemplary Sleep Statistics

In any of the technologies described herein, any variety of statisticscan be generated from adjusted source data. For example, sleepstatistics can be generated from adjusted source EEG data that has beenclassified into sleep states. Exemplary sleep statistics can includeinformation including sleep stage densities, number of sleep stageepisodes, sleep stage average duration, cycle time, interval timebetween sleep stages, sleep stage separation statistics, onset of sleep,rapid eye movement sleep latency, regression coefficients of trends,measures of statistical significance of trends, and the like.

EXAMPLE 18 Exemplary Implementation of a Method of Determining SleepStates in a Subject Over a Period of Time

Sleep is common and may be ubiquitous in all major taxa of the animalkingdom, but it is poorly understood. There is growing evidence fromhuman studies from a variety of low-level psychophysical perceptual andmotor tasks that sleep helps to remediate performance loss that isotherwise observed following task learning (Karni et al. 1994; Mednicket al. 2002; Mednick et at. 2003; Fenn et al. 2003). Animal studies haveprovided evidence of ‘replay’ during sleep, which may be a centralcomponent of the sleep process involved in consolidation of performance.

Recently, it has been shown that during sleep, robustus archistriatalis(RA) neurons of the zebra finch, Taeniopygia guttata, song systemrehearse song patterns spontaneously and respond to playback of thebird's own song (Dave & Margoliash, 2000). During song development inzebra finches, juvenile birds start changing singing patterns the dayfollowing exposure to new vocal material from tutors (Tchernichovski etal. 2001). There is no conclusive evidence though that song learning injuveniles or song maintenance in adult birds requires or benefits fromsleep.

Investigation of the possible role of sleep in song learning ormaintenance is hampered by the limited knowledge of sleep states inpasserine birds. Previous studies have not reported different phases ofsleep in the zebra finch (Nick & Konishi, 2002; Hahnloser et al., 2002).In contrast, studies in other birds, including passerine birds(Ayala-Guerrero et al., 1988; Szymczak et al., 1993; Rattenborg et al.,2004), have reported REM sleep in this phylum. Moreover, in rathippocampus different patterns of neuronal replay are known to takeplace during different phases of sleep (Buzsaki, 1989; Wilson &McNaughton, 1994; Louie & Wilson, 2001). Therefore, staging of sleep inzebra finches was investigated.

In order to determine the type, arrangement and location of electrodes,a series of acute experiments with birds anesthetized with urethane(20%, circa 90 1.11 over I hr) was first conducted. Optimal EEGrecordings, as judged by amplitude and reliability of signals, wereobtained using differentially paired thick platinum electrodes (A-Msystems, WA) touching the dura mater, with an additional ground over thecerebellum. The stereotaxic coordinates for the recording and groundelectrodes were respectively: (1.5R, 3L), (3R, 2L) and (0.5C, OL).

Five birds were then anesthetized and implanted with 3 mm long L-shapedplatinum electrodes at the aforementioned locations with the last 2 mmof the electrodes tangential to the dura mater along the medial-lateralaxis. The electrode impedance was 0.15 Ohms. In order to assessunihemispheric sleep, three birds were implanted with bilateral EEGelectrodes. Electrodes were secured at their base with dental acrylicand attached with fine copper wire (A-M systems, WA) to a headconnector. Birds were given 3 days to recover from the surgery and tohabituate to the recording environment.

During recordings, a light cable was attached linking the bird's head toan overhead mercury commutator (Drangonfly Inc, WV). This setup allowedthe bird relative freedom of movement within the cage and is preferableto restraining the animal since restraint-induced stress is known tomodify sleep architecture (Altman et al., 1972). During the dark phaseof the 16:8 light/dark cycle, electrophysiological recordings withdirect observation of sleeping birds were combined. Birds were bathed ininfrared (IR) light and monitored with an IR camera (Ikegama, Japan).Strategically placed mirrors facilitated detection of eye, head, andbody movements. EEGs were amplified by 1K, sampled at 1 kHz and filteredat 1-100 Hz. In one bird, which exhibited low frequency artifacts, thedata was filtered at 2-100 Hz. A 60 Hz notch filter was also used toimprove the signal-to-noise ratio.

In order to establish high confidence in the data analysis, the data wasscored both manually as well as automatically. Manual scoring relied onvisual inspection of 3 seconds EEG epochs in parallel with scoring ofovert behaviors such as eye, head and body movements. Manual scoringclassified each epoch as either REM, NREM (non-REM) or awake, includingthe artifacts. Automated scoring was restricted to the sleep data. TheSleep Parametric EEG Automated Recognition System (SPEARS) for stageseparation and quantification of single channel EEG data was used. EEGswere downsampled to 200 Hz, DC filtered, and analyzed over 3 secondsepochs using a 1 second sliding window to combine high spectral,temporal and statistical resolutions. In order to minimize spectralleakage and to increase statistical resolution in the frequency domain,EEG power spectra were computed over 2 orthogonal tapers following astandard multi-taper estimation technique (Thomson, 1982).

The 1-4 Hz (Delta) and 30-55 Hz (Gamma) frequency bands were selectedfor the stage classification. Delta and Gamma/Delta were respectivelyused to separate SWS from NSWS (Non-SWS) and REM from NREM. Theseparation was done with a k-means clustering algorithm and refined bythe inclusion of additional variables: the standard deviation and theabsolute values of the time derivative of Delta and of (Gamma/Delta).For each epoch, the time derivative was computed over the preceding andsuccessive epochs, using the Matlab “gradient” function. The initialseparation was done over the artifact free sleep data. Thereafter, sleepartifacts were attributed the same score as the first non-artifact epochimmediately following it, unless it was an awake epoch in which case thesleep artifact was given the score of the first preceding artifact freeepoch (which could not be an awake epoch for otherwise the artifactwould have been labeled as an awake artifact by manual scoring). Thisconvention did not significantly reduce the agreement rate with manualscoring (TABLE 1). It was important to include the sleep artifacts sinceremoving or not scoring them would respectively shrink or puncture sleepepisodes and thereby change the calculated density, average number ofepochs and length for each stage.

Following initial separation, the score of each epoch was smoothed usinga 5 second window in order to minimize the score contamination by briefartifacts which might not have been isolated by manual scoring. Epochsthat were scored neither as REM nor as SWS were labeled as intermediate(INTER). Conversely, any epoch that had been labeled as belonging toboth REM and SWS was relabeled as an outlier. There were very fewoutliers in the data (TABLE 1).

The REM, SWS and intermediate epochs can be visualized in a3-dimensional space (FIGS. 20-21) defined by the principal components ofthe 5 dimensional space defined by Delta, Gamma/Delta, the standarddeviation and the derivatives of Delta and (Gamma/Delta) (FIGS. 16-17).In each bird, a multivariate ANOVA on the 5-dimensional clustering spaceyielded a P<0.001 for the separation of REM, SWS and the intermediatestage.

Using the MATLAB “silhouette” function, the most representative examplesfor the SWS, REM, intermediate and awake epochs were automaticallygenerated (FIGS. 22, 23, 24, 25, and 26).

The agreement between manual and automated scoring was calculated byclassifying each epoch scored as REM by only the manual or the automatedscoring as an error. The general agreement rate was remarkably highgiven the high temporal resolution of the manual and automated scoring(TABLE 1).

Based on the automated analysis, the stage density (FIG. 28), averageepisode number (FIG. 30) and duration (FIG. 29), inter REM interval(FIG. 31) and stage transitions (FIG. 32) were computed (TABLE 1). Allanalyses were conducted in Matlab (MathWorks Inc, MA).

Table 1. Stage Statistics for 5 Nights of Sleep in 5 Birds.

Stage density, average episode duration and number and stage transitionswere determined. The percentage of transitions out of each stage towardsthe intermediate stage and the percentage of transitions out of theintermediate stage towards the other stages are shown. For thebihemispherically implanted birds (Animals 1-3), unihemispheric sleep isreported and the other statistics were computed over the hemisphere withthe most reliable data as determined by visual inspection of the EEG andvideo and the absence of outliers. The coefficient of regression wascomputed over the stage densities and inter-REM intervals for each hourand reflect the circadian distribution of SWS and REM (*=[r²>0.5 andp<0.05], §=[r²>0.5 and p=0.05), £ for values calculated for hours 2-8, εfor values calculated for hours 1-7). The agreement rate betweenautomated and manual scoring was determined with and without artifactrejection.

TABLE I Animal 1 Animal 2 Animal 3 Animal 4 Animal 5 Stage Density (%)SWS 44.44 30.14 41.03 25.71 36.59 INTER 30.96 30.34 37.46 31.70 37.49REM 21.06 30.51 15.79 30.77 15.12 AWAKE 3.54 8.94 5.73 11.83 10.80UNIHEM 0.09 0.59 0.65 N/A N/A OUTLIER 0.00 0.08 0.00 0.00 0.00 AverageEpisode Durathon (set) SWS 14.11 12.54 10.84 10.90 9.11 INTER 5.95 6.056.67 8.07 6.62 REM 9.84 10.11 8.53 16.98 9.21 AWAKE 11.37 12.10 9.3016.11 12.02 UNIHEM 3.38 3.84 3.19 N/A N/A OUTLIER N/A 2.22 N/A N/A N/ANumber of Episodes SWS 835 704 1092 629 1073 INTER 1378 1482 1623 11371601 REM 599 853 541 572 557 AWAKE 85 113 159 65 100 UNIHEM 8 44 59 N/AN/A OUTLIER 0 9 0 0 0 Transitions SWS-INTER (% SWS) 97.57 88.54 95.2193.93 97.05 REM-INTER (% REM) 85.49 90.34 86.06 92.64 83.75 AWAKE-INTER(% AWAKE) 60.49 71.94 72.15 27.79 64.16 OUT-INTER (% OUT) N/A 25.00 N/AN/A N/A INTER-SWS (% INTER) 56.57 43.06 63.23 51.31 66.49 INTER-REM (%INTER) 38.55 49.33 29.52 43.72 26.78 INTER-AWAKE (% INTER) 4.88 7.617.25 4.97 6.73 INTER-OUT (% INTER) N/A 0.00 N/A N/A N/A Regressioncoefficients Stage Density per hour SWS −6.20 −1.11 0.10 −5.46 −2.94INTER 1.57 1.93 −0.29 4.21 4.09 REM 4.89 3.16 2.44 8.08 4.77 AWAKE −0.25−3.99 −2.25 −6.83 −5.92 OUTLIER N/A 0.01 N/A N/A N/A Average EpisodeDuration per hour SWS −1.44 −0.37 0.59 −6.08 −1.11 INTER 0.05 0.24 0.211.37 0.31 REM 0.90 0.80 1.06 2.77 0.53 AWAKE −0.74 −0.89 −0.21 −6.34−0.92 OUTLIER N/A N/A N/A N/A N/A Number of Episodes per hour SWS −3.93−1.07 −6.13 −3.61 0.82 INTER 8.00 5.29 −8.11 2.93 14.46 REM 13.82 5.682.01 6.93 16.21 AWAKE −0.29 −1.54 −6.05 0.18 −1.61 OUTLIER N/A −0.04 N/AN/A N/A Inter-REM-interval per hour −7.56 −2.66 −2.27 −0.75 −15.10 CycleTime per hour 10.45 21.50 4.88 93.51 1.45 Agreement Rate (%) 89.94 76.7590.52 73.23 88.44 Agreement Rate - No artifacts (%) 90.08 76.93 91.5273.91 88.28

The analysis of the recordings indicate that zebra finches exhibit atleast three distinct phases of sleep: SWS, REM and intermediate sleep.SWS had a high amplitude EEG signal with significant power in the Deltarange (FIGS. 14-17). REM was characterized by a very low amplitude“awake-like” EEG signal (FIG. 23), typically about ±30 μV with higherpower in Gamma (FIGS. 14 and 15) than NREM, a feature that up to now hadonly been detected in mammals (Maloney et al., 1997; Cantero et al.,2004). The intermediate epochs had highly variable amplitudes, centeredaround ±50 μV and did not have significant power in either the Delta orGamma ranges (FIGS. 14, 15 and 24). The intermediate stage haspreviously only been observed in mammals (Gottesmann et al., 1984; Glinet al., 1991; Kirov & Moyanova, 2002). Both birds on normal circadianpatterns and shifted circadian schedules displayed these three sleepstages.

SWS epochs were longer than REM and intermediate episodes early in thenight and would, following a mammalian-like distribution, decrease induration (FIG. 29) throughout the night, leading to an overall decreasein stage density (FIG. 28) (TABLE 1).

During NREM birds breathe slowly and regularly; eye and head movementsdo not follow a stereotypical pattern and are quite distinct from thosein REM. We observed several instances when one eye was open and theother was closed. The hemisphere contralateral to the open eye displayeda low amplitude and high frequency EEG while the hemispherecontralateral to the closed eye displayed SWS oscillations. Theseinstances of unihemispheric sleep would usually account for less than 5%of the dark cycle (TABLE 1) and were more frequent in the light cycle.Such patterns of unihemispheric sleep have been previously detected inother species of birds, cetaceans and other marine mammals (Mukhametovet al., 1984; Mukhametov, 1987; Szymczak et al., 1996; Rattenborg etal., 1999; Lyamin et al., 2002).

REM episodes were typically brief early in the night and would becomelonger throughout the night (FIG. 29) as the number of episodes wouldincrease as well (FIG. 30), leading the Inter-REM intervals to exhibit adownward “mammalian-like” trend throughout the night (FIG. 31) (TABLE1). REM occurred reliably in conjunction with eye and subtle twitchinghead movements, as seen in other species (Siegel et al., 1999). The eyemovements were on the order of one saccade per second. The headmovements were not as reliable, but tended to follow the directionalmovement of the eyes when present. Head movements were not the result ofdisplacement of the head by the weight of the attached cable during REMneck muscle atonia because the head movements were observed inconjunction with eye movements in intact, un-tethered animals.

The intermediate epochs were brief and numerous. The intermediate statewas usually more stable throughout the night, in term of density (FIG.28), average epoch duration (FIG. 29) and average number of episodes perhour (FIG. 30) than REM and SWS. As is the case in mammals, theintermediate stage consistently acted as—but was not limited to—atransition phase between SWS and REM (FIG. 32) (TABLE 1).

In all birds, large peak-to-peak EEG transients lasting approximately500 milliseconds were detected in NREM (FIG. 26). These signals arereminiscent of the description of mammalian K-complexes (Rowan &Tolunsky, 2003). K-complexes have likely never been previously observedin a non-mammalian species.

In previous studies of zebra finch sleep EEG, only SWS has beenreported. In addition to finding a suitable location over which toimplant EEG electrodes, this study was successful in detecting NSWS (REMand the intermediate stage) presumably because the nature of the chronicrecording setup did not restrain the animals and obviated the need ofpharmacological agents such as melatonin to induce sleep. In one study(Mintz et al., 1998), infusion of melatonin was shown to induce SWS inpigeons. It is possible that melatonin might have a similar effect inzebra finches, thus reducing the amount of observable NSWS at night(Hahnloser et al. 2002).

The data analysis technique enabled resolving changes in power at thelower power, high frequencies, which was a key differentiating factorfor REM sleep detection. Moreover, the automated analysis restrictedmanual scoring to the awake state and artifacts, which are easilydetectable to a human scorer. Additionally, automated EEG scoring reliedon whole night statistics (Gervasoni et al.) rather than on arbitrarilydefined threshold, maximum likelihood methods or supervised nonlinearclassifiers all of which tend to reflect and impose a human bias on thedata analysis.

The results imply that mammalian-like sleep features have evolved inparallel in both mammals and birds. The basic pattern of interdigitationbetween Delta and Gamma power activation described herein (FIGS. 14 and15) is highly similar to the one observed in the mammalian cortex duringsleep (Destexhe, Contreras & Steriade, 1999). Furthermore, some of thesignals we have observed have been specifically attributed to themammalian cortex (Amzica & Steriade, 1998). Birds are however devoid ofa large laminar cortex, raising the possibility that the cortex might beat best sufficient but not necessary for the development ofmammalian-like sleep features. Conversely, it is conceivable that birdsdo indeed possess a mammalian cortex homolog in a non-laminar form(Karten, 1997). Future work at the cellular and molecular levels will beneeded to assess which of these highly intriguing possibilities provesto be correct.

References Cited:

Altman et al. Psychon. Sci. 26 (1972), pp. 152-154. Amzica & Steriade.Neuroscience. 1998 February; 82(3):671-86. Ayala-Guerrero et al. PhysiolBehav. 1988; 43(5):585-9. Buzsaki. Neuroscience. 1989; 31(3):551-70.

Cantero et al. Neuroimage. 2004 July; 22(3):1271-80.

Dave & Margoliash. Science. 2000 Oct. 27; 290(5492):812-6. Destexhe,Contreras & Steriade. 1999 Jun. 1; 19(11):4595-608. Fenn et al. Nature.2003 Oct. 9; 425(6958):614-6. Gervasoni et al. J. Neurosci. 2004 Dec. 8;24(49):11137-47. Glin et al. Physiol Behav. 1991 November; 50(5):951-3.Gottesmann et al. J Physiol (Paris). 1984; 79(5):365-72.

Hahnloser et al. Nature. 2002 Scp 5; 419(6902):65-70. Karni et al.Science. 1994 Jul. 29; 265(5172):679-682

Karten. Proc Natl Acad Sci USA. 1997 Apr. 1; 94(7):2800-4. Khazipov etal. Society for Neuroscience Abstracts 2004. Kirov & Moyanova. NeurosciLett. 2002 Apr. 5; 322(2):134-6. Louie & Wilson. Neuron. 2001 January;29(1):145-56. Lyamin et al. Behav Brain Res. 2002 Feb. 1; 129(1-2):125-9Maloney et al. Neuroscience. 1997 January; 76(2):541-55. Mednick et al.Nat Neurosci. 2002 July; 5(7):677-81 Mednick et al. Nat Neurosci. 2003July; 6(7):697-8. Mintz et al. Neurosci Lett. 1998 Dec. 18; 258(2):61-4.

Mukhametov et al. Zh Vyssh New Deiat Im I P Pavlova. 1984 March-April;34(2):259-64.

Mukhametov. Neurosci Lett. 1987 Aug. 18; 79(1-2):128-32.

Nick & Konishi. Proc Natl Acad Sci USA. 2001 Nov. 20; 98(24):14012-6.

Rattenborg et al. Behav Brain Res. 1999 Nov. 15; 105(2):163-72.Rattenborg et al. PLoS Biol. 2004 July; 2(7):E212.

Rowan & Tolusnky. “Primer of EEG”. Butterworth Heinemann. ElsevierScience 2003

Siegel et al. Neuroscience. 1999; 91(1):391-400.

Szymczak et al. Physiol Behav. 1993 June; 53(6):1201-10. Szymczak et al.Physiol Behav. 1996 October; 60(4):1115-20. Tchernichovski et al.Science. 2001 Mar. 30; 291(5513):2564-9. Thomson, Proceedings of theIEEE, Vol. 70 (1982), pp. 1055-1096. Wilson & McNaughton. Science. 1993Aug. 20; 261(5124):1055-8

EXAMPLE 19 Exemplary Method for Determining Sleep States in a SubjectOver a Period of Time

FIG. 35 shows yet another exemplary method 3500 for determining sleepstates in a subject over a period of time. The method 3500 incorporatesa wide variety of techniques described herein.

EXAMPLE 20 Exemplary Transformation Techniques

There are a wide variety of data transformation methods used in signalprocessing to determine power for a variety of frequencies in timeseries data. As described herein, transformation methods can includemulti-taper transform, Fourier transform, wavelet transform. Any othertransformation method for measuring power for a variety of frequenciesrepresented in a plurality of time series or epochs in a source signalcan be used.

EXAMPLE 21 Exemplary Computational Methods for Differentiating Groups ofData

There are a wide variety of clustering and classification methods usedin computational signal processing to differentiate data into distinctclasses. As described herein, the clustering method used is k-meansclustering but any computational signal processing method fordifferentiating groups of data could be used. Similarly, classificationmethods such as component analysis (e.g., principle and independentcomponent analysis) are used as described herein.

An overview of computational methods is provided below.

Clustering (or cluster analysis) is unsupervised learning where theclasses are unknown a priori and the goal is to discover these classesfrom data. For example, the identification of new tumor classes usinggene expression profiles is a form of unsupervised learning.

Classification (or class prediction) is a supervised learning methodwhere the classes are predefined and the goal is to understand the basisfor the classification from a set of labeled objects and build apredictor for future unlabeled observations. For example, theclassification of malignancies into known classes is a form ofsupervised learning.

Clustering:

Clustering involves several distinct steps:

Defusing a suitable distance between objects

Selecting a applying a clustering algorithm.

Clustering procedures commonly fall into two categories: hierarchicalmethods and partitioning methods. Hierarchical methods can be eitherdivisive (top-down) or agglomerative (bottom-up). Hierarchicalclustering methods produce a tree or dendrogram. Hierarchical methodsprovide a hierarchy of clusters, from the smallest, where all objectsare in one cluster, through to the largest set, where each observationis in its own cluster

Partitioning methods usually require the specification of the number ofclusters. Then, a mechanism for apportioning objects to clusters must bedetermined. These methods partition the data into a prespecified numberk of mutually exclusive and exhaustive groups. The method iterativelyreallocates the observations to clusters until some criterion is met(e.g. minimize within-cluster sumsof-squares). Examples of partitioningmethods include k-means clustering, Partitioning around medoids (PAM),self organizing maps (SOM), and model-based clustering.

Most methods used in practice are agglomerative hierarchical methods, ina large part due to the availability of efficient exact algorithms.However both clustering methods have their advantages and disadvantages.Hierarchical advantages include fast computation, at least foragglomerative clustering, and disadvantages include that they are rigidand cannot be corrected later for erroneous decisions made earlier inthe method. Partitioning advantages include that such methods canprovide clusters that (approximately) satisfy an optimality criterion,and disadvantages include that one needs an initial k and the methodscan take long computation time.

In summary, clustering is a more difficult problem than classifying fora variety of reasons including the following:

there is no learning set of labeled observations

the number of groups is usually unknown

implicitly, one must have already selected both the relevant featuresand distance measures used in clustering methods.

Classification:

Techniques involving statistics, machine learning, and psychometrics canbe used. Examples of classifiers include logistic regression,discriminant analysis (linear and quadratic), principle componentanalysis (PCA), nearest neighbor classifiers (k-nearest neighbor),classification and regression trees (CART), prediction analysis formicroarrays, neural networks and multinomial log-linear models, supportvector machines, aggregated classifiers (bagging, boosting, forests),and evolutionary algorithms.

Logistic Regression:

Logistic regression is a variation of linear regression which is usedwhen the dependent (response) variable is a dichotomous variable (i.e.,it takes only two values, which usually represent the occurrence ornon-occurrence of some outcome event, usually coded as 0 or 1) and theindependent (input) variables are continuous, categorical, or both. Forexample, in a medical study, the patient survives or dies, or a clinicalsample is positive or negative for a certain viral antibody.

Unlike ordinary regression, logistic regression does not directly modela dependent variable as a linear combination of dependent variables, nordoes it assume that the dependent variable is normally distributed.Logistic regression instead models a function of the probability ofevent occurrence as a linear combination of the explanatory variables.For logistic regression, the function relating the probabilities to theexplanatory variables in this way is the logistic function, which has asigmoid or S shape when plotted against the values of the linearcombination of the explanatory variables.

Logistic regression is used in classification by fitting the logisticregression model to data and classifying the various explanatoryvariable patterns based on their fitted probabilities. Classificationsof subsequent data are then based on their covariate patterns andestimated probabilities.

Discriminant Analysis:

In summary discriminant analysis represents samples as points in spaceand then classifies the points. Linear discriminant analysis (LDA) fmdsan optimal plane surface that best separates points that belong to twoclasses. Quadratic discriminant analysis (QDA) fmds an optimal curved(quadratic) surface instead. Both methods seek to minimize some form ofclassification error.

Fisher Linear Discriminant Analysis (FLDA or LDA):

LDA fmds linear combinations (discriminant variables) of data with largeratios of between-groups to within-groups sums of squares and predictsthe class of an observation x by the class whose mean vector is closestto x in terms of the discriminant variables. Advantages of LDA includethat it is simple and intuitive where the predicted class of a test caseis the class with the closest mean and it is easy to implement with agood performance in practice. Disadvantages of LDA include thefollowing:

linear discriminant boundaries may not be flexible enough

features may have different distributions within classes

in the case of too many features, performance may degrade rapidly due toover parameterization and high variance of parameter estimates.

Nearest Neighbor Classifiers:

Nearest neighbor methods are based on a measure of distance betweenobservations, such as the Euclidean distance or one minus thecorrelation between two data sets. K-nearest neighbor classifiers workby classifying an observation x as follows:

-   -   find the k observations in the learning set that are closest to        x.    -   predict the class of x by majority vote, i.e., choose the class        that is most common among these k neighbors. Simple classifiers        with k=1 can generally be quite successful. A large number of        irrelevant or noise variables with little or no relevance can        substantially degrade the performance of a nearest neighbor        classifier.

Classification Trees:

Classification trees can be used, fir example, to split a sample intotwo sub-samples according to some rule (feature variable threshold).Each sub-sample can be further split, and so on. Binary tree structuredclassifiers are constructed by repeated splits of subsets (nodes) intotwo descendant subsets. Each terminal subset of the tree is assigned aclass label and the resulting partition corresponds to the classifier.The three main aspects of tree construction include selection of splits(at each node, the split that maximize the decrease in impurity ischosen), decision to declare a node terminal or to continue splitting(to grow a large tree, the tree is selectively pruned upwards getting adecreasing sequence of subtrees), and assignment of each terminal nodeto a class (the class the minimizes the resubstitution estimate of themisclassification probability is chosen for each terminal node).

Prediction Analysis for Microarrays:

These methods utilize nearest shrunken centroid methodology. First, astandardized centroid for each class is computed. Then each classcentroid is shrunk toward the overall centroid for all classes by theso-called threshold (chosen by the user). Shrinkage consists of movingthe centroid towards zero by threshold, setting it equal to zero if ithits zero.

Artificial Neural Networks

The key element of the artificial neural network (ANN) model is thenovel structure of the information processing system. It is composed ofmany highly interconnected processing elements that are analogous toneurons and are tied together with weighted connections that areanalogous to synapses. As with all classification methods, once the ANNis trained on known samples, it will be able to predict samplesautomatically.

Support Vector Machines:

Support Vector Machines are learning machines that can perform binaryclassification (pattern recognition) and real valued functionapproximation (regression estimation) tasks. Support Vector Machinesnon-linearly map their n-dimensional input space into a higherdimensional feature space. In this high dimensional feature space alinear classifier is constructed.

Aggregating Classifiers:

This method works by aggregating predictors built from perturbedversions of a learning set. In classification, the multiple versions ofthe predictor are aggregated by voting. Bootstrapping is the simplestform of bagging in which perturbed learning sets of the same size as theoriginal learning set are non-parametric bootstrap replicates of thelearning set, i.e., drawn at random with replacement from the learningset. Parametric bootstrapping involves perturbed learning sets that aregenerated according to a mixture of multivariate Gaussian distributions.Random Foresting is a combination of tree classifiers (or other), whereeach tree depends on the value of a random vector for all trees in theforest. In boosting, classifiers are constructed on weighted version thetraining set, which are dependent on previous classification results.Initially, all objects have equal weights, and the first classifier isconstructed on this data set. Then, weights are changed according to theperformance of the classifier. Erroneously classified objects get largerweights, and the next classifier is boosted on the reweighted trainingset. In this way, a sequence of training sets and classifiers isobtained, which is then combined by simple majority voting or byweighted majority voting in the decision.

EXAMPLE 22 Exemplary Sleep Data Presenter

In any of the examples herein, an electronic or paper-based report basedon sleep state data can be presented. Such reports can includecustomized sleep state information, sleep state statistics, pathologicalconditions, medication and/or chemical effects on sleep, and the likefor a subject. Recommendations for screening tests, behavioral changes,and the like can also be presented. Although particular sleep data andlow frequency information results are shown in some examples, othersleep data presenters and visualizations of data can be used.

EXAMPLE 23 Exemplary Sleep State Information for Subjects

Exemplary sleep state information can be obtained from a variety ofsubjects using any of the technologies described herein. FIG. 33includes a screenshot 3300 of an exemplary visualization of classifiedanesthesized states of an anesthetized cat based on analyzed EEGspectrogram data. For example, in screenshot 3300, a SWS classificationcorresponds to a deep anesthesized state, a REM sleep classificationcorresponds to a light anesthesized state, and an INTER sleepclassification corresponds to an intermediate anesthesized state. Insuch a manner, the technologies described herein can be utilized todetermine anesthesized states in a human or other mammalian subject.FIG. 34 includes a screenshot 3400 of an exemplary visualization ofclassified sleep states of a human subject based on analyzed EEGspectrogram data.

EXAMPLE 24 Exemplary Advantages and Applications of Technologies

The speed at which this data analysis can be performed, the customizedand unsupervised nature of analysis, and the ability to extractpreviously disregarded or unanalyzed low power frequency informationmake this methodology particularly attractive to a variety of fields ofstudy. The technology can be highly adaptable using a variable number ofstates, a variable number of identification rules, adaptablecalibration, variable time resolution, and variable spectral resolution.Adjusting source data to generate adjusted source data can be especiallyapplicable to analyzing animal signal data in testing for pathologicalconditions and medication and chemical effects. In any of the examplesherein, low amplitude but highly variable frequency data can beextracted and analyzed (e.g., discovering temporal patterns in data).Applications can include diverse uses from analyzing stock market data(e.g., analyzing fluctuations in penny stocks to determine commonvariability otherwise disregarded due to small price changes) toaccessing encoded data (e.g., Morse code data stored in low power, veryhigh or very low frequencies within sound waves) to analyzing visualimages with several spatial frequencies. Similarly, the technologiesdescribed herein can be used to determine customized sleep qualitydeterminations for a subject via sleep state information generated.

In any of the examples herein, the methods can be applied to source datareceived from one channel or multiple channels. The methods can beapplied independently to source data from multiple channels withcomparison made between the channels. For example, unihemispheric sleepcan be determined from independent EEG channel data received from eachhemisphere of a brain. FIG. 40 shows a screen shot 4000 ofunihernispheric sleep determined from independent EEG channel datareceived from each hemisphere of a bird's brain. Alternatively, themethods can be simultaneously applied to source data from multiplechannels, thereby analyzing combined multiple channel source data. Forexample, EEG channel data and EMG channel data for a subject can besimultaneously analyzed to determine awake versus REM sleep stateswhereby a REM designated sleep state from analysis of EEG data can bereassigned as an awake sleep state if the EMG data falls into a highamplitude cluster.

Further, in any of the examples herein, methods such as denoising sourceseparation (dss) and the like can be used in combination with themethods described herein to determine sleep states. For example, dss canuse low frequency information to determine REM sleep.

While the techniques described herein can be particularily valuable foranalyzing low power frequency information they can also be applied toclustering and determining sleep stages from any variety of signalsincluding signals wherein the high and low frequencies have the samepower distributions. Additionally, techniques pertaining to spectrogramanalysis, stage classification and confidence measures can be usedindependently of one another.

EXAMPLE 25 Exemplary Visualizations of Data

In any of the techniques described herein, exemplary visualizations ofdata can utilize colors to depict different aspects of that data. Forexample, classified data (e.g., sleep state classifications such as REM,SWS, and INTER) can be color coded for each classification state forvisualization of the classified data. Alternatively, greyscale can beused to code for each classification state for visualization of theclassified data.

EXAMPLE 26 Exemplary Computer System for Conducting Analysis

FIG. 36 and the following discussion provide a brief, generaldescription of a suitable computing environment for the software (forexample, computer programs) described above. The methods described abovecan be implemented in computer-executable instructions (for example,organized in program modules). The program modules can include theroutines, programs, objects, components, and data structures thatperform the tasks and implement the data types for implementing thetechniques described above.

While FIG. 36 shows a typical configuration of a desktop computer, thetechnologies may be implemented in other computer system configurations,including multiprocessor systems, microprocessor-based or programmableconsumer electronics, minicomputers, mainframe computers, and the like.The technologies may also be used in distributed computing environmentswhere tasks are performed in parallel by processing devices to enhanceperformance. For example, tasks can be performed simultaneously onmultiple computers, multiple processors in a single computer, or both.In a distributed computing environment, program modules may be locatedin both local and remote memory storage devices. For example, code canbe stored on a local machine/server for access through the Internet,whereby data from assays can be uploaded and processed by the localmachine/server and the results provided for printing and/or downloading.

The computer system shown in FIG. 36 is suitable for implementing thetechnologies described herein and includes a computer 3620, with aprocessing unit 3621, a system memory 3622, and a system bus 3623 thatinterconnects various system components, including the system memory tothe processing unit 3621. The system bus may comprise any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using a bus architecture. The systemmemory includes read only memory (ROM) 3624 and random access memory(RAM) 3625. A nonvolatile system (for example, BIOS) can be stored inROM 3624 and contains the basic routines for transferring informationbetween elements within the personal computer 3620, such as duringstart-up. The personal computer 3620 can further include a hard diskdrive 3627, a magnetic disk drive 3628, for example, to read from orwrite to a removable disk 3629, and an optical disk drive 3630, forexample, for reading a CD-ROM disk 3631 or to read from or write toother optical media. The hard disk drive 3627, magnetic disk drive 3628,and optical disk 3630 are connected to the system bus 3623 by a harddisk drive interface 3632, a magnetic disk drive interface 3633, and anoptical drive interface 3634, respectively. The drives and theirassociated computer-readable media provide nonvolatile storage of data,data structures, computer-executable instructions (including programcode such as dynamic link libraries and executable files), and the likefor the personal computer 3620. Although the description ofcomputer-readable media above refers to a hard disk, a removablemagnetic disk, and a CD, it can also include other types of media thatare readable by a computer, such as magnetic cassettes, flash memorycards, DVDs, and the like.

A number of program modules may be stored in the drives and RAM 3625,including an operating system 3635, one or more application programs3636, other program modules 3637, and program data 3638. A user mayenter commands and information into the personal computer 3620 through akeyboard 3640 and pointing device, such as a mouse 3642. Other inputdevices (not shown) may include a microphone, joystick, game pad,satellite dish, scanner, or the like. These and other input devices areoften connected to the processing unit 3621 through a serial portinterface 3646 that is coupled to the system bus, but may be connectedby other interfaces, such as a parallel port, game port, or a universalserial bus (USB). A monitor 3647 or other type of display device is alsoconnected to the system bus 3623 via an interface, such as a displaycontroller or video adapter 3648. In addition to the monitor, personalcomputers typically include other peripheral output devices (not shown),such as speakers and printers.

The above computer system is provided merely as an example. Thetechnologies can be implemented in a wide variety of otherconfigurations. Further, a wide variety of approaches for collecting andanalyzing source data are possible. For example, the data can becollected and analyzed, and the results presented on different computersystems as appropriate. In addition, various software aspects can beimplemented in hardware, and vice versa. Further, paper-based approachesto the technologies are possible, including, for example, purelypaper-based approaches that utilize instructions for interpretation ofalgorithms, as well as partially paper-based approaches that utilizescanning technologies and data analysis software.

EXAMPLE 27 Exemplary Computer-Implemented Methods

Any of the computer-implemented methods described herein can beperformed by software executed by software in an automated system (forexample, a computer system). Fully-automatic (for example, without humanintervention) or semi-automatic operation (for example, computerprocessing assisted by human intervention) can be supported. Userintervention may be desired in some cases, such as to adjust parametersor consider results.

Such software can be stored on one or more computer-readable mediacomprising computer-executable instructions for performing the describedactions. Such media can be tangible (e.g., physical) media.

Having illustrated and described the principles of the invention inexemplary embodiments, it should be apparent to those skilled in the artthat the described examples are illustrative embodiments and can bemodified in arrangement and detail without departing from suchprinciples. Techniques from any of the examples can be incorporated intoone or more of any of the other examples.

Another embodiment uses a dual normalization for even further dynamicrange increase. This embodiment explains, and relies on, data from humansleep subjects, rather than birds as in some of the previousembodiments. Moreover, any of the applications described above for theprevious embodiments are equally applicable for this embodiment, as arethe techniques of normalization and clustering.

This embodiment uses many of the characteristics of the previousembodiments and also adds some refinements. The embodiment operates toanalyze brain wave activities. The signals from a brainwave, e.g., anEEG, typically follows the characteristic where the amount of power inthe brain wave is related to, e.g., proportional to 1/f, where f is thefrequency of the brain wave: The amount of power is inverselyproportional to the frequency. As explained with reference to previousembodiments, this 1/f spectral distribution has tended to obscure thehigher frequency portions of the signal, since those higher frequencyportions of the signals had smaller voltage amplitudes.

Human observers who observed the waves representing the EEGs havehistorically been unable to ascertain any substantial informationrelative to the higher frequency. Many reasons for this have beenpostulated by the inventors. One reason is that higher frequencies ofbrainwave activities have been more filtered from the skull, because thephysical structure of the skull acts as a low pass filter.

Previous embodiments have shown how normalization, for example using Zscoring, allowed analysis of more information from the brainwave signal.The analysis which was previously carried out normalized powerinformation across frequencies. The normalization preferably used Zscoring, but any other kind of data normalization can be used. Thenormalization which is used is preferably unitless, like Z scoring. Aswell-known in the art, z scoring can be used to normalize a distributionwithout changing a shape of the envelope of the distribution. The zscores are essentially changed to units of standard deviation. Each zscore normalized unit reflects the amount of power in the signal,relative to the average of the signal. The scores are converted intomean deviation form, by subtracting the mean from each score. The scoresare then normalized relative to standard deviation. All of the z scorednormalized units have standard deviations that are equal to unity.

While the above describes normalization using Z scores, it should beunderstood that other normalizations can also be carried out, includingT scoring, and others.

The above embodiments describe normalizing the power at every frequencywithin a specified range. The range may be from 0, to 100 hz, or to 128hz, or to 500 hz. The range of frequencies is only restricted by thesampling rate. With an exemplary sampling rate of 30 KHz, an analysis upto 15 KHz can be done.

According to the present embodiment, an additional normalization iscarried out which normalizes the power across time for each frequency.This results in information which has been normalized across frequenciesand across time being used to create a doubly normalized spectrogram.

This embodiment can obtain additional information from brainwave data,and the embodiment describes automatically detecting different periodsof sleep from the analyzed data. The periods of sleep that can bedetected can include, but are not limited to, short wave sleep (SWS),rapid eye movement sleep (REM), intermediate sleep (IIS) andwakefulness. According to an important feature, a single channel ofbrainwave activity (that is obtained from a single location on the humanskull) is used for the analysis.

The operation is carried out according to the flowchart of FIG. 41,which may be executed in any of the computer devices described herein,or may be executed across a network or in any other known way. At 4100,data is obtained. As described above, the obtained data can be onechannel of EEG information from a human or other subject. The EEG dataas obtained can be collected, for example, using a 256 Hz sampling rate,or can be sampled at a higher rate. The data is divided into epochs, forexample 30 second epochs, and characterized according to frequency.

At 4110, a first frequency normalization is carried out. The powerinformation is normalized using a z scoring technique on each frequencybin. In the embodiment, the bins may extend from one to 100 Hz and 30bins per hertz. The normalization occurs across time. This creates anormalized spectrogram or NS, in which each frequency band from thesignal has substantially the same weight. In the embodiment, each 30second epoch is represented by a “preferred frequency” which is thefrequency with the largest z score within that epoch.

This creates a special frequency space called the preferred frequencyspace. FIG. 42A illustrates the raw spectrogram, and FIG. 42Billustrates the normalized spectrum. Each epoch, e.g., a 30 secondsegment in FIG. 43, or or a 1 second sliding window epoch in FIG. 44, isrepresented by the frequency with the largest z score. FIG. 44illustrates how this broadly separates into different patterns.

Analysis of how those patterns are formed and allow analysis of thecharacteristics of the patterns. For example, the W or wakefulness statehas been found by analysis to be characterized by a band in the alphaband, or 7 to 12 Hz and sometimes by a band in the beta (15 to 25 Hz).

Intermediate states display Delta values in the 1 to 4 Hz range, and thespindle frequencies in 12 to 15 Hz. These also show activity of thehigher frequencies and the gamma range 3-90 Hz. Surprisingly, REM statedefines compact bands at Delta and Theta frequencies, and short wavesleep was dominated by diffuse broad-spectrum activity.

Different sleep states, therefore, can be defined according to adiscrimination function, where the discrimination function looks forcertain activity in certain areas, and non-activity in other areas. Thefunction may evaluate sleep states according to which of the frequencyat areas have activity and which do not have activity.

More generally, however, any form of dynamic spectral scoring can becarried out on the compensated data. The discrimination function mayrequire specific values, or may simply require a certain amount ofactivity to be present or not present, in each of a plurality offrequency ranges. The discrimination function may simply match envelopesof frequency response. The discrimination function may also look atspectral fragmentation and temporal fragmentation.

4120 illustrates a second normalization which is carried out acrossfrequencies. The second normalization at 4120 produces a doublynormalized spectrogram. This produces a new frequency space, in whichthe bands become even more apparent. The second normalization is shownas FIG. 42C, where bands show as lighter values, representing thepositive values, while darker regions will tend to have negative values.

The doubly normalized spectrogram values can be used to form filtersthat maximally separate the values within the space. FIG. 43 illustratesa graph of preferred frequency as a function of time, showing thedifferent clusters of frequencies.

4130 illustrates a clustering technique which is carried out on thedoubly normalized frequency. For example, the clustering technique maybe a K means technique as described in the previous embodiments. Theclusters form groups, as shown in FIG. 43. FIG. 44 illustrates how theareas between different states, such as boundary 4400, form multipledifferent clusters. Each cluster can represent a sleep state.

The clusters are actually multi dimensional clusters, which canthemselves be graphed to find additional information, as shown in FIG.45. The number of dimensions can depend on the number of clusteringvariables. This illustrates how the doubly normalized spectrogram alsoallows many more measurement characteristics. FIG. 45 is actually athree-dimensional graph, of different characteristics, and can allowdetection of the different states. The analysis, however, reveals thatslow wave sleep is more unstable and time and frequency than rapid eyemovement sleep or wakefulness. Intermediate sleep often forms a bridgeto and from the short wave sleep.

Measurement of the average spread in normalized power across frequencywhich illustrates the spectral fragmentation is also possible, as shownin FIG. 46 illustrates the spectral fragmentation. Fragmentation valuescan alternatively be based on temporal fragmentation for the differentstates may also be used as part of the discrimination function.

For example:

Using Z and ZZ to correspond to the NS and 2NS values respectively:

w_filter=mean(ZZ(12-15 Hz))+mean(ZZ(1-4 Hz))+mean(ZZ(4-7 Hz)).

nrem_filter=mean(ZZ(60-100 Hz))+mean(ZZ(4-7 Hz))−[mean(ZZ(12-15Hz))+mean(ZZ(25-60 Hz))+mean(ZZ(15-25 Hz))]

sws_filter=mean(Z(4-7 Hz))+mean(Z(7-12 Hz))

The fragmentation values are as follows:

Spectral_frag=mean(abs(grad_f(ZZ(1-100 Hz))));

Spectral_temp=mean(abs(grad_t(ZZ(1-100 Hz))));

Where grad_f and grad_t correspond to the two-dimensional nearestneighbor gradients of ZZ.

These two functions are evaluated on the doubly normalized spectrum,relying on homogeneous increases in gain at all frequencies as causedmovement artifacts in NREM sleep and W would lead to abnormally elevatedfragmentation values in the singly normalized spectrum.

These fragmentation values may be used as part of the discriminationfunction. Importantly, and as described above, this discriminationfunction is typically not apparent from any previous analysis technique,including manual techniques.

The computation may be characterized by segmenting, or may useoverlapping windows or a sliding window, to increase the temporalregistration. This enables many techniques that have never been possiblebefore. By characterizing on-the-fly, this enables distinguishing usingthe dynamic spectral scoring, between sleep states and awake statesusing the brainwave signature alone.

Another aspect includes a machine which automatically obtains EEGinformation, and includes a computer that analyzes the EEG informationto determine information about the sleep state. For example, theinformation may include the actual sleep state, or other parts of thesleep state. The computer may also include nonvolatile memory therein tostore the information indicative of the sleep state, and may include,for example, a wireless network connection to allow sending theinformation indicative of the sleep state to a remote device. The usercan wear the machine, or an electrode that is connected to the machine,in order to characterize his or her sleep.

The above has described how information can be used to determine sleepstates. These techniques may also be used for other applicationsincluding characterizing sleep states, and other techniques.Applications may include determination of whether a patient has takencertain kinds of drugs based on their sleep state, and based onvariables that were previously determined as changing in brain functionbased on those sleep states. Another application can analyze brain wavesignals to determine alcohol consumption, e.g., forming a system thatcan be used as a “breathalyzer”.

The general structure and techniques, and more specific embodimentswhich can be used to effect different ways of carrying out the moregeneral goals are described herein.

Although only a few embodiments have been disclosed in detail above,other embodiments are possible and the inventors intend these to beencompassed within this specification. The specification describesspecific examples to accomplish a more general goal that may beaccomplished in another way. This disclosure is intended to beexemplary, and the claims are intended to cover any modification oralternative which might be predictable to a person having ordinary skillin the art. For example, other applications are possible, and otherforms of discrimination functions and characterization is possible.While the above extensively described characterizing the frequency interms of its “preferred frequency”, it should be understood that morerigorous characterization of the information may be possible. Also,while the above only refers to determining sleep states from the EEGdata, and refers to only a few different kinds of determination of sleepstates, it should be understood that other applications arecontemplated.

Also, the inventors intend that only those claims which use the words“means for” are intended to be interpreted under 35 USC 112, sixthparagraph. Moreover, no limitations from the specification are intendedto be read into any claims, unless those limitations are expresslyincluded in the claims.

The computers described herein may be any kind of computer, eithergeneral purpose, or some specific purpose computer such as aworkstation. The computer may be a Pentium class computer, runningWindows XP or Linux, or may be a Macintosh computer. The computer mayalso be a handheld computer, such as a PDA, cell phone, or laptop.

The programs may be written in C, or Java, Brew or any other programminglanguage. The programs may be resident on a storage medium, e.g.,magnetic or optical, e.g. the computer hard drive, a removable disk ormedia such as a memory stick or SD media, or other removable medium. Theprograms may also be run over a network, for example, with a server orother machine sending signals to the local machine, which allows thelocal machine to carry out the operations described herein.

1. A method, comprising: obtaining data indicative of brainwaveactivity; normalizing at least one frequency range of said data tochange a power level of the data in said at least one frequency rangerelative to data in another frequency range, to form normalized dataindicative of brainwave activity; a second normalizing of the data, toform double normalized data, wherein said second normalizing comprisesnormalizing frequencies across time; and analyzing said doublenormalized data indicative of brainwave activity utilizing a computingdevice to provide at least one parameter indicative of sleep state fromsaid analyzing.
 2. A method as in claim 1, wherein said first and secondnormalizing each use Z scoring for the normalizing.
 3. A method as inclaim 1, further comprising defining a discrimination function whichrepresents characteristics of the said double normalized data for aplurality of different sleep states, and using said discriminationfunction to determine a sleep state from said double normalized data. 4.A method as in claim 3, wherein said discrimination function is afunction that is in terms of frequencies which are present in specifiedranges and not present in specified other ranges, to define a sleepstate.
 5. A method as in claim 1, further comprising characterizing apreferred frequency as a frequency which has the highest normalizedvalue in any specified time, and analyzing the preferred frequency todetermine said at least one parameter.
 6. A method as in claim 5,further comprising defining a discrimination function as a function ofpreferred frequency, where a discrimination function defines a sleepstate in terms of frequencies which are present, and frequencies whichare not present.
 7. A method as in claim 1, wherein analyzing saiddouble normalized data indicative of brainwave activity furthercomprises analyzing a fragmentation of the double normalized data.
 8. Amethod for determining sleep states in a subject over a period of timecomprising: receiving brain wave data for the subject over the period oftime, wherein the brain wave data exhibits lower dynamic range for powerin at least one low power first frequency range in a frequency spectrumas compared to a second frequency range in the frequency spectrum;segmenting the brain wave data into one or more epochs; weightingfrequency power of the one or more epochs across time utilizing acomputing device, wherein the weighting comprises increasing the dynamicrange for power within the low power frequency range of the frequencyspectrum as compared to the second frequency range, thereby generatingone or more frequency weighted epochs; and providing classified sleepstates of the subject based on the one or more frequency weightedepochs.
 9. The method as in claim 8 wherein classifying sleep states inthe subject comprises: clustering the one or more frequency weightedepochs; and assigning sleep state designations to the one or morefrequency weighted epochs according to the clustering; and presentingthe sleep state designations as indicative of sleep states in thesubject for the period of time represented by the one or more frequencyweighted epochs.
 10. The method of claim 8 wherein clustering the one ormore frequency weighted epochs comprises k-means clustering.
 11. Themethod of claim 8 further comprising pretreating the brain wave datawith component analysis.
 12. The method of claim 8 wherein classifyingsleep states in the subject comprises applying independent componentanalysis to the one or more frequency weighted epochs.
 13. The method ofclaim 8 wherein classifying sleep states further comprises incorporatingmanually determined sleep states.
 14. The method of claim 8 whereinassigning sleep state designations to the one or more frequency weightedepochs comprises: determining a slow wave sleep designation from anon-slow wave sleep designation based at least on low frequencyinformation; and determining a rapid eye movement sleep designation froma non-rapid eye movement sleep designation based at least on highfrequency information.
 15. The method of claim 14 further comprisingassigning a slow wave sleep designation to an epoch that has significantweighted power at low frequencies.
 16. The method of claim 14 furthercomprising assigning a rapid eye movement sleep designation to an epochwith significant weighted power at high frequency.
 17. The method ofclaim 8 wherein assigning sleep state designations to the one or morefrequency weighted epochs further comprises applying a smoothing windowto the one or more weighted epochs, wherein the smoothing can compriseaveraging sleep state designations across the one or more weightedepochs.
 18. The method of claim 8 further comprising presenting one ormore frequency weighted epochs as canonical spectra representative ofthe sleep state in the subject for the period of time represented by theone or more epochs having similar sleep state designations.
 19. Themethod of claim 18 further comprising analyzing the canonical spectrawith independent component analysis to establish sleep stateclassification confidence.
 20. The method of claim 8 further comprisingpresenting sleep statistics for the subject according to the sleep statedesignations of the one or more frequency weighted epochs.
 21. Themethod as in claim 8, further comprising second weighting power tonormalize the data according to a second dimension, prior to saidclassifying to form doubly normalized data.
 22. The method as in claim21, wherein said second weighting comprises normalizing at least onefrequency across time.
 23. A method as in claim 21, wherein saidweighting and said second weighting each use Z scoring to carry outnormalizing.
 24. A method as in claim 21, further comprising defining adiscrimination function which represents characteristics of the doublenormalized data for a plurality of different sleep states, and usingsaid discrimination function to determine a sleep state from said doublenormalized data.
 25. A method as in claim 24, wherein saiddiscrimination function is a function that is in terms of frequencieswhich are present in specified ranges and not present in specified otherranges, to define a sleep state.
 26. A method as in claim 21, furthercomprising characterizing a preferred frequency as a frequency which hasthe highest normalized value in any specified time, and analyzing thepreferred frequency to determine said at least one parameter.
 27. Amethod as in claim 26, further comprising defining a discriminationfunction as a function of said preferred frequency, where adiscrimination function defines a sleep state in terms of frequencieswhich are present, and frequencies which are not present.
 28. A methodas in claim 21, further comprising analyzing a spectral fragmentation ofthe doubly normalized data, and using the spectral fragmentation as partof said analyzing.
 29. A method as in claim 21, further comprisinganalyzing a temporal fragmentation of the doubly normalized data, andusing the temporal fragmentation as part of said analyzing.